Learning Microarray Cancer Datasets by Random Forests and Support Vector Machines

Abstract

Analyzing gene expression data from microarray devices has many important applications in medicine and biology: the diagnosis of disease, accurate prognosis for particular patients, and understanding the response of a disease to drugs, to name a few. Two classifiers, random forests and support vector machines are studied in application to micro array cancer data sets. Performance of classifiers with different numbers of genes were evaluated in hope to find out if a smaller number of good genes gives a better classification rate.